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arXiv AI By Lucas Da Mota Bruno, Jiahao Sim, Yoshinobu Hagiwara

Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots

Read the original on arXiv AI →

The paper introduces an LLM chaining architecture for General Purpose Service Robots that splits instruction classification and action generation into two stages, cutting prompt length by about 45% and boosting planning consistency. Evaluation on 100 synthetic GPSR commands across three language models shows consistent improvements over single-prompt methods, with up to +37 percentage points gain on local models. Real‑robot trials on the Toyota HSR confirm that while planning success improves, execution-layer failures remain the main obstacle to full task completion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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